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Advances in Social Sciences Research Journal – Vol. 12, No. 2
Publication Date: February 25, 2025
DOI:10.14738/assrj.122.18347.
Abou Afach, S., & Kibbi, I. (2025). Exploring Coding as a Catalyst for Critical Thinking Development: A Case Study in a Private School
in Lebanon. Advances in Social Sciences Research Journal, 12(2). 190-206.
Services for Science and Education – United Kingdom
Exploring Coding as a Catalyst for Critical Thinking Development:
A Case Study in a Private School in Lebanon
Sara Abou Afach
Doctoral School of Literature,
Humanities & Social Sciences, Lebanon
Ibrahim Kibbi
Doctoral School of Literature,
Humanities & Social Sciences, Lebanon
ABSTRACT
This study explores the impact of integrating coding into classroom instruction on the
development of critical thinking skills among grade fifth learners in beirut Lebanese. By
providing learners with opportunities for hands-on experimentation, collaborative
planning, and self-reflection, the study aimed to foster a deeper level of critical thinking.
Although the learners initially preferred traditional textbook-based learning, significant
improvements in critical thinking were observed in the experimental group (127
learners), demonstrating the effectiveness of an experiential learning approach. The
teacher played a crucial role in guiding learners, offering tailored resources, and ensuring
that each learner had the opportunity to build knowledge both individually and as part of
a team. The study highlights the importance of spreading coding integration across
multiple sessions, rather than limiting it to a single weekly session, to maximize learners
engagement and learning outcomes such as developing their critical thinking skills.
Additionally, the research emphasizes the need for a unified definition of critical thinking
skills across the school to ensure systematic development. The findings suggest that
coding, when integrated into subject-specific lessons, can develop essential problem- solving and computational thinking skills, making it a valuable tool in primary education.
The study recommends further research in both private and public school settings to
compare the effects of coding on critical thinking. This research contributes to the limited
body of literature on coding's impact within the Lebanese educational context and
provides a framework for future studies on skill-based learning and coding
implementation.
Keywords: Critical Thinking, Computational Thinking integration, Project based Hands-on
Learning, Skill-Based Learning, Teacher Pedagogy.
INTRODUCTION & LITERATURE
Coding and computational thinking in education are not new concepts; they have been taught
since the 1960s. However, technological advancements have significantly changed how these
subjects are delivered. In today's software-driven world, teaching coding has become essential
as it equips learners with Information and Communication Technology (ICT) skills [1]. Modern
programming applications primarily use visual programming languages designed to simplify
coding instruction, making it more accessible to learners. By reducing unnecessary syntax,
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Abou Afach, S., & Kibbi, I. (2025). Exploring Coding as a Catalyst for Critical Thinking Development: A Case Study in a Private School in Lebanon.
Advances in Social Sciences Research Journal, 12(2). 190-206.
URL: http://dx.doi.org/10.14738/assrj.122.18347
visual programming languages help students focus on coding logic and structure, thereby
reducing cognitive load [2].
The MIT Media Lab in the United States pioneered the development of Scratch and ScratchJr,
which aim to create transformative learning experiences that empower individuals to redesign
their lives [3]. These visual programming applications facilitate the development of
computational thinking by allowing students to use their native or international language (e.g.,
English), making the learning process more intuitive.
Research Aim and Research Question
The study aims to examine the effect of coding on fifth graders critical thinking (CT) and provide
them with enhanced learning opportunities beyond regular classroom sessions. The research
is guided by the following question:
How Does Coding Influence the Development of Critical Thinking Skills in Fifth Graders?
Teaching coding in primary school provides significant advantages over introducing it at a later
stage. Early exposure to coding is comparable to learning a new language, as it helps students
develop problem-solving strategies, design projects, and generate innovative ideas [4][5].
Furthermore, teachers play a crucial role in integrating coding into real-life contexts by linking
classroom instruction with practical applications [6][7].
Recent studies emphasize the importance of introducing coding in primary education, arguing
that structured coding instruction at an early age fosters logical thinking and problem-solving
abilities [5]. One effective approach is to integrate coding into different subjects through
project-based learning, enabling students to see meaningful connections between coding and
real-world scenarios. This approach enhances student engagement and improves their ability
to sequence logical steps in problem-solving.
Literature Review: Sciences Technology Engineering Mathematics and Computer
Science (STEM-C)
A study conducted in Croatia and Puerto Rico aimed to increase K-12 learners' participation in
Science, Technology, Engineering, Mathematics, and Computer Science (STEM-C) fields. The
research introduced both short-term and long-term programs designed to promote early
engagement with STEM disciplines and support students' transition to college.
The Short-Term Program
In K-8 classrooms, introductory science visits exposed students to various STEM fields. These
visits included interactive workshops, programming exercises, and participation in the Hour of
Code initiative. Additionally, science fairs for grades 9-12 encouraged students to develop
research skills, present findings, and receive constructive feedback [8].
The Long-Term Program
For grades 10-12, the study implemented a year-long program consisting of Saturday club
meetings and a seven-week summer camp. During these sessions, students engaged in hands- on STEM projects, working in teams under the mentorship of educators. The program required
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students to dedicate eight hours per day, five days per week, culminating in presentations
evaluated by peers and faculty members.
By the end of both programs, informal feedback was collected through paintings (K-8) and open
essays (grades 9-12). The findings demonstrated significant positive outcomes: in Croatia, 500
learners participated in the initiative within two years, while in Puerto Rico, the 15-year
program impacted over 4,550 students from 225 schools. Moreover, 100% of participants
transitioned to college, with 85% pursuing STEM-C-related fields.
Findings and Recommendations
The study concluded that early exposure to computer science significantly benefits students,
even when introduced through drag-and-drop programming techniques [8]. However,
researchers recommended a more continuous approach, advocating for an integrated K-12
curriculum rather than separating short- and long-term initiatives.
While the study yielded promising results, some methodological details were unclear. For
example, it did not specify which coding games or activities were used for K-8 students, possibly
due to annual curriculum modifications. Future research should provide a more detailed
methodology to strengthen the study’s replicability.
Overall, the findings highlight the importance of early computer science education and the need
for structured, long-term initiatives that support students throughout their academic journey.
A well-supported primary education curriculum can help develop a generation of learners who
are not only STEM-conscious but also proficient in coding, logical reasoning, and algorithmic
thinking.
Figure 1: Literature Review Summary
METHODOLOGY
This study follows a quantitative research design and was conducted with 127 Grade 5 learners,
aged 10–11 years, in a private school located in the capital city of Lebanon, Beirut. The school
is situated in a densely populated area and has a relatively high number of students enrolled
across all K-12 levels. The participants in this study, like most learners in Lebanon, receive
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Abou Afach, S., & Kibbi, I. (2025). Exploring Coding as a Catalyst for Critical Thinking Development: A Case Study in a Private School in Lebanon.
Advances in Social Sciences Research Journal, 12(2). 190-206.
URL: http://dx.doi.org/10.14738/assrj.122.18347
instruction in scientific subjects in English. Consequently, the intervention, including the
questionnaire, was conducted in English.
The school was selected as a convenient sample, as it agreed to implement the intervention,
aligning with its vision. While middle school learners in this institution already receive coding
instruction, the school saw this study as an opportunity to pilot coding education at the primary
level. All sessions were conducted in person within the school premises.
Conceptual Framework
Successfully integrating coding into any classroom requires adjustments before and during
implementation. One of the key pillars of such a change is understanding relevant learning
theories. Additionally, differences in technology use and educational standards were addressed
to facilitate smooth classroom implementation.
The frameworks guiding this study included the Technological Pedagogical Content Knowledge
(TPACK) model and Project-Based Learning (PBL). These frameworks addressed the social and
pedagogical aspects of implementation. Based on these insights, a tailored framework was
developed to suit the Lebanese context. The study's significance lies in its focus on developing
computational thinking skills, preparing learners for future careers, and assessing their
abilities based on skills and personal engagement rather than rote memorization.
Theoretical Framework
A theoretical model was established to outline the study’s independent (IV) and dependent (DV)
variables. This began with identifying the research problem and its objectives. The TPACK
framework served as the primary guide for technology integration, emphasizing three core
components:
• Content Knowledge (CK): Teachers' depth of knowledge in the subject matter.
• Pedagogical Knowledge (PK): Effective teaching strategies, classroom management,
and learner engagement.
• Technological Knowledge (TK): Learners' ability to use digital tools effectively.
Data Collection Method
A questionnaire was used to collect quantitative data, which was later analyzed using SPSS. The
primary goal was to assess learners’ perceptions of their critical thinking, problem-solving,
coding abilities, and hands-on skills throughout the intervention. The questionnaire employed
a four-level Likert scale, ranging from "Strongly Disagree" to "Strongly Agree," and was used as
both a pre- and post-assessment tool.
The questionnaire was chosen for its efficiency in gathering large-scale data from multiple
respondents simultaneously. Since there was limited research on coding and critical thinking
at the primary education level, the questionnaire was designed to align with the study’s
objectives, research questions, and the Lebanese curriculum while accommodating the
learners’ cognitive levels.
The questionnaire measured critical thinking by evaluating reasoning skills and alignment
between thought processes and actions. It comprised closed-ended questions grouped into four
categories: Critical Thinking, Problem-Solving, Planning, and Technology. Closed-ended
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questions were preferred for their efficiency in data collection and analysis, as they provided
clearer and more structured responses compared to open-ended questions.
To ensure clarity and appropriateness for the target audience, all questions were written in
simple English, avoiding complex or emotionally charged wording. Positively phrased
questions were used to enhance comprehension and prevent response bias. A four-level Likert
scale was chosen to avoid neutral responses, ensuring clear insights into learners’ perspectives.
Research suggests that an asymmetric Likert scale compels respondents to take a stance rather
than opting for a neutral position [12].
Questionnaire Validity and Reliability
To ensure validity, both face and content validity assessments were conducted. Content validity
ensured that the questionnaire effectively measured the intended concepts, while face validity
was achieved through expert review. A panel of domain experts reviewed the questionnaire to
identify ambiguous or misleading questions and suggested refinements in wording and
sequencing.
A pilot test was conducted with Grade 5 learners from a similar private school to evaluate the
questionnaire’s structure, language, completion time, and clarity. Minor modifications were
made based on learners' feedback regarding unfamiliar words.
To test reliability, a Cronbach’s Alpha test was performed to assess internal consistency. The
results from the pilot test (N=16) were as follows:
Table 1 Cronbach Alpha Results for Questionnaire (Pilot)
Questionnaire Pilot
Critical thinking 0.705
Problem solving 0.707
Planning 0.718
Technology 0.746
These values indicate a strong level of internal consistency, confirming the reliability of the
questionnaire as a data collection instrument.
Project Rubric
In addition to the questionnaire, a project rubric was used as a quantitative tool to assess group
performance and track progress over time. Initially inspired by the “Maryland STEM” rubric, it
was adapted to align with the study’s objectives and research questions. The rubric employed
a four-level Likert scale (1–4), with 1 being the lowest and 4 representing excellence in
measured criteria. The rubric assessed three primary categories: Skills, Technology, and
Coding. It was designed to facilitate accurate and structured evaluation by teachers.
Project Rubric Validity and Reliability
The rubric was validated by a panel of five professors specializing in education, technology,
science, and computer science. Their feedback ensured that the rubric authentically measured
learners’ abilities.
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post-questionnaire of the experimental group. The Correlation test was performed to
understand if there was a relationship between the variables. The same tests were performed
on the post-questionnaire of the control group. The paired tests were performed to determine
value of the mean difference of the two observances.
To study the effect of coding and critical thinking skills, a Chi-Square test was performed on the
pre-questionnaire and post-questionnaire of the experimental group.
Pre-Questionnaire Experimental Group
Table 4 Critical thinking & technology: Crosstabulation pre-questionnaire
Critical Thinking and Technology Crosstabulation
Technology Total
Totally Disagree Disagree Agree Totally Agree
Critical thinking Disagree Count 1 1 4 4 10
% 10.0% 10.0% 40.0% 40.0% 100.0%
Agree Count 0 1 13 11 25
% 0.0% 4.0% 52.0% 44.0% 100.0%
Totally Agree Count 0 3 4 15 22
% 0.0% 13.6% 18.2% 68.2% 100.0%
Total Count 1 5 21 30 57
% 1.8% 8.8% 36.8% 52.6% 100.0%
The results of (3x4) Crosstabulation between the critical thinking and technology in pre- questionnaire. The highest answers were for those who “Totally Agree” N=15 (68.2%), followed
by “Agree” N=13 (52.0%), N=1 (10%) learner answered “Disagree” on both questions.
Table 5 Critical thinking & technology: Chi-Square Project Rubric
Chi-Square Tests
Value df Asymptotic
Significance (2-
sided)
Exact Sig.
(2-sided)
Exact Sig.
(1-sided)
Point
Probability
Pearson Chi-Square 11.293a 6 .080 .060
Likelihood Ratio 10.417 6 .108 .115
Fisher's Exact Test 10.039 .068
Linear-by-Linear
Association
2.424b 1 .119 .131 .078 .031
N of Valid Cases 57
a. 7 cells (58.3%) have expected count less than 5. The minimum expected count is .18. b. The standardized
statistics is 1.557
In the pre-questionnaire, the analysis showed 7 cells that had an expected count less than 5, so
an exact significance text was selected for Pearson's Chi-Square. There was no statistical
significance between critical thinking and technology X2(6, N= 57) = 11.29 exact p=0.06>0.05.
Post-Questionnaire Experimental Group
The same tests (Crosstabulation and Chi-Square) were performed on the post-questionnaire.
Table 6 Critical thinking & technology: Crosstabulation post questionnaire
Critical thinking & Technology Crosstabulation
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Abou Afach, S., & Kibbi, I. (2025). Exploring Coding as a Catalyst for Critical Thinking Development: A Case Study in a Private School in Lebanon.
Advances in Social Sciences Research Journal, 12(2). 190-206.
URL: http://dx.doi.org/10.14738/assrj.122.18347
Technology Total
Disagree Agree Totally Agree
Critical Thinking Disagree Count 5 4 2 11
% within Critical thinking 45.5% 36.4% 18.2% 100.0%
Agree Count 1 14 6 21
% within Critical thinking 4.8% 66.7% 28.6% 100.0%
Totally Agree Count 1 10 15 26
% within Critical thinking 3.8% 38.5% 57.7% 100.0%
Total Count 7 28 23 58
% within Critical thinking 12.1% 48.3% 39.7% 100.0%
The results of the square (3x3) Crosstabulation between the critical thinking and technology in
post-questionnaire revealed that the highest answers were for those who “Totally Agree”
(N=15), (57.7%), then “Agree” (N=14), (66.7%), followed by “Disagree” N=5 (45.5%).
Table 7 Critical thinking & technology: Chi-Square results post-questionnaire
Chi-Square Tests
Value df Asymptotic
Significance (2-sided)
Exact Sig.
(2-sided)
Exact Sig.
(1-sided)
Point
Probability
Pearson Chi-Square 18.920a 4 .001 .001
Likelihood Ratio 15.533 4 .004 .006
Fisher's Exact Test 14.351 .003
Linear-by-Linear
Association
11.127b 1 .001 .001 .001 .000
N of Valid Cases 58
a. 4 cells (44.4%) have expected count less than 5. The minimum expected count is 1.33. b. The standardized
statistics is 3.336
In the post-questionnaire, the analysis showed 4 cells that had an expected count less than 5,
so an exact significance text was selected for Pearson’s Chi-Square. There was statistical
significance between critical thinking and technology X2 (4, N=58) = 18.920 exact P=0.01<0.05;
thus, phi value was analyzed.
Table 6 Critical thinking & technology: Phi Value post-questionnaire
Symmetric Measures
Value Asymptotic
Standardized
Errora
Approximate
Tb
Approximate
Significance
Exact
Significance
Nominal
by
Nominal
Phi .571 .001 .001
Cramer's V .404 .001 .001
Interval by
Interval
Pearson's R .442 .123 3.686 .001c .001
Ordinal by
Ordinal
Spearman
Correlation
.416 .122 3.426 .001c .001
N of Valid Cases 58
a. Not assuming the null hypothesis.
b. Using the asymptotic standard error assuming the null hypothesis.
c. Based on normal approximation.
Phi value=0.571 showed there was a strong relationship between critical thinking and coding.
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The Spearman Correlation test r=0.416 showed a moderate Correlation between critical
thinking and coding.
The results of the pre-questionnaire were not statistically significant between coding and
critical thinking; however, after the intervention, the post-questionnaire showed statistically
significant values; the coding had an impact on critical thinking.
Post-questionnaire Experimental Group
The same tests (Crosstabulation and Chi-Square) were performed on the post-questionnaire.
Table 7 Critical thinking & technology: Crosstabulation post questionnaire -
Experimental Group
Critical Thinking* Technology Crosstabulation
Technology Total
Disagree Agree Totally Agree
Critical thinking Disagree Count 5 4 2 11
% within Critical thinking 45.5% 36.4% 18.2% 100.0%
Agree Count 1 14 6 21
% within Critical thinking 4.8% 66.7% 28.6% 100.0%
Totally Agree Count 1 10 15 26
% within Critical thinking 3.8% 38.5% 57.7% 100.0%
Total Count 7 28 23 58
% within Critical thinking 12.1% 48.3% 39.7% 100.0%
The results of the square (3x3) Crosstabulation between the critical thinking and technology in
post-questionnaire revealed that the highest answers were for those who “Totally Agree”
(N=15), (57.7%), then “Agree” (N=14), (66.7%), followed by “Disagree” N=5 (45.5%).
Table 8 Critical thinking & technology: Chi-Square results post-questionnaire
Chi-Square Tests
Value df Asymptotic
Significance (2-sided)
Exact Sig.
(2-sided)
Exact Sig.
(1-sided)
Point
Probability
Pearson Chi-Square 18.920a 4 .001 .001
Likelihood Ratio 15.533 4 .004 .006
Fisher's Exact Test 14.351 .003
Linear-by-Linear
Association
11.127b 1 .001 .001 .001 .000
N of Valid Cases 58
a. 4 cells (44.4%) have expected count less than 5. The minimum expected count is 1.33.
b. The standardized statistic is 3.336.
In the post-questionnaire, the analysis showed 4 cells that had an expected count less than 5,
so an exact significance text was selected for Pearson’s Chi-Square. There was statistical
significance between critical thinking and technology X2 (4, N=58) = 18.920 exact P=0.01<0.05;
thus, phi value was analyzed.
Table 9 Critical thinking & technology: Phi Value post-questionnaire
Symmetric Measures